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Concrete linings containing cold joint defects may crack or detach under the aerodynamic fatigue loading generated by high-speed train operation, which posing a serious threat to the normal operation of high-speed trains. However, there is currently no simulation method specifically for fatigue damage of concrete linings containing cold joints. Based on the Roe-Siegmund cycle cohesive force model, a cohesive force fatigue damage elements were developed. A large dataset was constructed through numerical simulation software to build a BP neural network for back-calculated parameter of cohesive force fatigue damage elements. By combining experimental data, fatigue damage parameters corresponding to different pouring interval cold joints were back-calculated. These back-calculated parameters were then incorporated into the numerical model to compare simulation results with experimental results to validate the applicability of cohesive force fatigue damage elements and back propagation neural networks (BP neural network). The research results show that the difference between the fatigue life and fracture process calculated by numerical simulation and experimental data is small, verifying the applicability of the method proposed in this paper. The pouring interval directly affects the initial strength of the cold joint interface and the starting conditions of fatigue damage. The possibility of fatigue damage and fracture of concrete components containing cold joints increases with the increase of pouring interval, while the variability of fatigue life decreases with the increase of pouring interval. Interface strength and thickness are the main factors affecting the possibility of fatigue damage occurrence and the variability of fatigue life. The research results can be used to analyze the damage and cracking status of concrete linings containing cold joints under aerodynamic fatigue loading. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is a multifactorial disease influenced by genetic, environmental, and immunological factors. Genetic polymorphisms in the interleukin-10 (IL-10) gene have been implicated in PCa susceptibility, development, and progression. This study aims to assess the contributions of three IL-10 promoter single nucleotide polymorphisms (SNPs), A-1082G (rs1800896), T-819C (rs3021097), and A-592C (rs1800872), to the risk of PCa in Taiwan. The three IL-10 genotypes were determined using PCR-RFLP methodology and were evaluated for their contributions to PCa risk among 218 PCa patients and 436 non-PCa controls. None of the three IL-10 SNPs were significantly associated with the risks of PCa (p all > 0.05) in the overall analyses. However, the GG at rs1800896 combined with smoking behavior was found to significantly increase the risk of PCa by 3.90-fold (95% confidence interval [95% CI] = 1.28–11.89, p = 0.0231). In addition, the rs1800896 AG and GGs were found to be correlated with the late stages of PCa (odds ratio [OR] = 1.90 and 6.42, 95% CI = 1.05–3.45 and 2.30–17.89, p = 0.0452 and 0.0003, respectively). The IL-10 promoter SNP, A-1082G (rs1800896), might be a risk factor for PCa development among smokers and those at late stages of the disease. These findings should be validated in larger and more diverse populations. [ABSTRACT FROM AUTHOR]

This study aims explore the feasibility of using neural network (NNs) and deep learning to diagnose three common respiratory diseases with few symptom words. These three diseases are nasopharyngitis, upper respiratory infection, and bronchitis/bronchiolitis. Through natural language processing, the symptom word vectors are encoded by GPT-2 and classified by the last linear layer of the NN. The experimental results are promising, showing that this model achieves a high performance in predicting all three diseases. They revealed 90% accuracy, which suggests the implications of the developed model, highlighting its potential use in assisting patients' understanding of their conditions via a remote diagnosis. Unlike previous studies that have focused on extracting various categories of information from medical records, this study directly extracts sequential features from unstructured text data, reducing the effort required for data pre-processing. [ABSTRACT FROM AUTHOR]

Lung cancer (LC) stands as the foremost cause of cancer-related fatality rates worldwide. Early diagnosis significantly enhances patient survival rate. Nowadays, low-dose computed tomography (LDCT) is widely employed on the chest as a tool for large-scale lung cancer screening. Nonetheless, a large amount of chest radiographs creates an onerous burden for radiologists. Some computer-aided diagnostic (CAD) tools can provide insight to the use of medical images for diagnosis and can augment diagnostic speed. However, due to the variation in the parameter settings across different patients, substantial discrepancies in image voxels persist. We found that different voxel sizes can create a compromise between model generalization and diagnostic efficacy. This study investigates the performance disparities of diagnostic models trained on original images and LDCT images reconstructed to different voxel sizes while making isotropic. We examined the ability of our method to differentiate between benign and malignant nodules. Using 11 features, a support vector machine (SVM) was trained on LDCT images using an isotropic voxel with a side length of 1.5 mm for 225 patients in-house. The result yields a favorable model performance with an accuracy of 0.9596 and an area under the receiver operating characteristic curve (ROC/AUC) of 0.9855. In addition, to furnish CAD tools for clinical application, future research including LDCT images from multi-centers is encouraged. [ABSTRACT FROM AUTHOR]

The use of deep learning methods for the automatic detection and quantification of bone metastases in bone scan images holds significant clinical value. A fast and accurate automated system for segmenting bone metastatic lesions can assist clinical physicians in diagnosis. In this study, a small internal dataset comprising 100 breast cancer patients (90 cases of bone metastasis and 10 cases of non-metastasis) and 100 prostate cancer patients (50 cases of bone metastasis and 50 cases of non-metastasis) was used for model training. Initially, all image labels were binary. We used the Otsu thresholding method or negative mining to generate a non-metastasis mask, thereby transforming the image labels into three classes. We adopted the Double U-Net as the baseline model and made modifications to its output activation function. We changed the activation function to SoftMax to accommodate multi-class segmentation. Several methods were used to enhance model performance, including background pre-processing to remove background information, adding negative samples to improve model precision, and using transfer learning to leverage shared features between two datasets, which enhances the model's performance. The performance was investigated via 10-fold cross-validation and computed on a pixel-level scale. The best model we achieved had a precision of 69.96%, a sensitivity of 63.55%, and an F1-score of 66.60%. Compared to the baseline model, this represents an 8.40% improvement in precision, a 0.56% improvement in sensitivity, and a 4.33% improvement in the F1-score. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation. [ABSTRACT FROM AUTHOR]

Bone Scan Index (BSI) is an image biomarker for quantifying bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related research focus on binary classification in bone scintigraphy: having metastasis or none. Rare studies focus on pixel-wise segmentation. This study compares three advanced convolutional neural network (CNN) based models to explore bone segmentation on a dataset in-house. The best model is Mask R-CNN, which reaches the precision, sensitivity, and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, and 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability of clinical use on bone segmentation. [ABSTRACT FROM AUTHOR]

Red, green, and blue light InxGa1−xN multiple quantum wells have been grown on GaN/γ-LiAlO2 microdisk substrates by plasma-assisted molecular beam epitaxy. We established a mechanism to optimize the self-assembly growth with ball-stick model for InxGa1-xN multiple quantum well microdisks by bottom-up nanotechnology. We showed that three different red, green, and blue lighting micro-LEDs can be made of one single material (InxGa1-xN) solely by tuning the indium content. We also demonstrated that one can fabricate a beautiful InxGa1-xN-QW microdisk by choosing an appropriate buffer layer for optoelectronic applications. [ABSTRACT FROM AUTHOR]

Mg-doped GaN was grown by plasma-assisted molecular beam epitaxy (PAMBE) on a Fe-doped GaN template substrate by employing a shutter-controlled process. The transition from n-type to p-type conductivity of Mg-doped GaN in relation to the N/Ga flux ratio was studied. The highest p-type carrier concentration in this series was 3.12 × 1018 cm−3 under the most N-rich condition. By modulating the shutters of different effusion cells for the shutter-controlled process, a wide growth window for p-type GaN was obtained. It was found that the presence of Mg flux effectively prevents the formation of structural defects in GaN epi-layers, resulting in the improvement of crystal quality and carrier mobility. [ABSTRACT FROM AUTHOR]

The air-booster vacuum preloading method has been applied to slurry ground improvement. It is based on the conventional vacuum preloading method but with an additional injection of pressurised air into the soil via pre-installed conductors. The drainage effect of air-booster vacuum preloading has been demonstrated by past studies; however, direct observations of the real-time behaviour of slurries subjected to boosted air remain lacking. This study used a combined monitoring technique that included particle image velocimetry, pore water/air pressure gauges, a vortex flowmeter and an electronic balance to conduct a laboratory test of air-booster vacuum consolidation of dredged slurry. The tests allowed analyses of (1) the real-time displacement field of the slurry, (2) the pressure–flux relationship of the pressurised air, and (3) the pore water pressure responses during air boosting. The first aspect allowed direct observation of small-crack initialisation and propagation during pressurisation; while the latter two confirmed the crack initiation based on drops in air and pore water pressures. The measured crack initiation pressure was verified by comparison with theoretical predictions. The results demonstrate that pressurised air induces cracks in soil, which promote the drainage consolidation of dredged slurry. [ABSTRACT FROM AUTHOR]

Background: Hepatic arterial infusion chemotherapy (HAIC) has been proven to be an effective treatment for advanced HCC. In this study, we present our single-center experience of implementing combined sorafenib and HAIC treatment for these patients and compare the treatment benefit with that of sorafenib alone. Methods: This was a retrospective single-center study. Our study included 71 patients who started taking sorafenib between 2019 and 2020 at Changhua Christian Hospital in order to treat advanced HCC or as a salvage treatment after the failure of a previous treatment for HCC. Of these patients, 40 received combined HAIC and sorafenib treatment. The efficacy of sorafenib alone or in combination with HAIC was measured in regard to overall survival and progression-free survival. Multivariate regression analysis was performed to identify factors associated with overall survival and progression-free survival. Results: HAIC combined with sorafenib treatment and sorafenib alone resulted in different outcomes. The combination treatment resulted in a better image response and objective response rate. Moreover, among the patients aged under 65 years old and male patients, the combination therapy resulted in a better progression-free survival than sorafenib alone. A tumor size ≥ 3 cm, AFP > 400, and ascites were associated with a poor progression-free survival among young patients. However, the overall survival of these two groups showed no significant difference. Conclusions: Combined HAIC and sorafenib treatment showed a treatment effect equivalent to that of sorafenib alone as a salvage treatment modality used to treat patients with advanced HCC or with experience of a previously failed treatment. [ABSTRACT FROM AUTHOR]

The five-year overall survival rate of patients without neck lymph node recurrence is over 50% higher than those with lymph node metastasis. This study aims to investigate the prognostic impact of computed tomogram (CT)-based radiomics on the outcome of metastatic neck lymph nodes in patients with head and neck cancer (HNC) receiving definitive radiotherapy or chemoradiotherapy for organ preservation. The pretreatment 18F-FDG PET/CT of 79 HNC patients was retrospectively analyzed with radiomics extractors. The imbalanced data was processed using two techniques: over-sampling and under-sampling, after which the prediction model was established with a machine learning model using the XGBoost algorithm. The imbalanced dataset strategies slightly decreased the specificity but greatly improved the sensitivity. To have a higher chance of predicting neck cancer recurrence, however, clinical data combined with CT-based radiomics provides the best prediction effect. The original dataset performed was as follows: accuracy = 0.76 ± 0.07, sensitivity = 0.44 ± 0.22, specificity = 0.88 ± 0.06. After we used the over-sampling technique, the accuracy, sensitivity, and specificity values were 0.80 ± 0.05, 0.67 ± 0.11, and 0.84 ± 0.05, respectively. Furthermore, after using the under-sampling technique, the accuracy, sensitivity, and specificity values were 0.71 ± 0.09, 0.73 ± 0.13, and 0.70 ± 0.13, respectively. The outcome of metastatic neck lymph nodes in patients with HNC receiving radiotherapy for organ preservation can be predicted based on the results of machine learning. This way, patients can be treated alternatively. A further external validation study is required to verify our findings. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular management and risk stratification of patients is an important issue in clinics. Patients who have experienced an adverse cardiac event are concerned for their future and want to know the survival probability. Methods: We trained eight state-of-the-art CNN models using polar maps of myocardial perfusion imaging (MPI), gender, lung/heart ratio, and patient age for 5-year survival prediction after an adverse cardiac event based on a cohort of 862 patients who had experienced adverse cardiac events and stress/rest MPIs. The CNN model outcome is to predict a patient's survival 5 years after a cardiac event, i.e., two classes, either yes or no. Results: The best accuracy of all the CNN prediction models was 0.70 (median value), which resulted from ResNet-50V2, using image as the input in the baseline experiment. All the CNN models had better performance after using frequency spectra as the input. The accuracy increment was about 7~9%. Conclusions: This is the first trial to use pure rest/stress MPI polar maps and limited clinical data to predict patients' 5-year survival based on CNN models and deep learning. The study shows the feasibility of using frequency spectra rather than images, which might increase the performance of CNNs. [ABSTRACT FROM AUTHOR]

Titanium alloys have now become the first choice of tubing material used in the harsh oil- and gas-exploitation environment, while the interaction of force and medium is a serious threat to the safety and reliability of titanium alloy in service. In this paper, different stresses were applied to TC4 titanium alloy by four-point bending stress fixture, and the corrosion behavior of TC4 titanium alloy was studied by high-temperature and high-pressure simulation experiments and electrochemical techniques, and the microscopic morphologies and chemical composition of the surface film layer on the specimen were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray energy-dispersive spectroscopy (EDS), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS), to reveal the corrosion-resistance mechanism of TC4 titanium alloy under different stress-loading conditions. The results showed that the pits appeared on the specimens loaded with elastic stress, but the degree of pitting corrosion was still lighter, and the surface film layer showed n-type semiconductor properties with cation selective permeability. While the pits on the specimens loaded with plastic stress were deeper and wider in size, and the semiconductor type of the surface film layer changed to p-type, it was easier for anions such as Cl− and CO32− to adsorb on, destroy, and pass through the protective film and then to contact with the matrix, resulting in a decrease in corrosion resistance of TC4 titanium alloy. [ABSTRACT FROM AUTHOR]

This article presents, for the first time, the efficacy and curing depth analysis of photo-thermal dual polymerization in metal (Fe) polymer composites for 3D printing of a three-component (A/B/M) system based on the proposed mechanism of our group, in which the co initiators A and B are Irgacure-369 and charge–transfer complexes (CTC), respectively, and the monomer M is filled by Fe. Our formulas show the depth of curing (Zc) is an increasing function of the light intensity, but a decreasing function of the Fe and photoinitiator concentrations. Zc is enhanced by the additive [B], which produces extra thermal radical for polymerization under high temperature. The heat (or temperature) increase in the system has two components: (i) due to the light absorption of Fe filler and (ii) heat released from the exothermic photopolymerization of the monomer. The heat is transported to the additive (or co-initiator) [B] to produce extra radicals and enhance the monomer conversion function (CF). The Fe filler leads to a temperature increase but also limits the light penetration, leading to lower CF and Zc, which could be overcome by the additive initiator [B] in thick polymers. Optimal Fe for maximal CF and Zc are explored theoretically. Measured data are analyzed based on our derived formulas. [ABSTRACT FROM AUTHOR]

The nearly lattice-matched InxGa1−xN/InyAl1−yN epi-layers were grown on a GaN template by plasma-assisted molecular beam epitaxy with a metal modulation technique. The band-gap energy of InxGa1−xN QW in photoluminescence measurement was estimated to be 2.89 eV and the indium concentration (x) was 14.8%. In X-ray photoelectric spectroscopy, we obtained an indium concentration (y) in the InyAl1−yN barrier of 25.9% and the band-offset was estimated to be 4.31 eV. From the atomic layer measurements from high-resolution transmission electron microscopy, the lattice misfit between the InxGa1−xN QW and InyAl1−yN barrier was 0.71%. The lattice-matched InxGa1−xN/InyAl1−yN QWs can therefore be evaluated from the band profiles of III-nitrides for engineering of full-visible-light emitting diode in optoelectronic application. [ABSTRACT FROM AUTHOR]

Background: The accurate determination of cell confluence is a critical step for generating reasonable results of designed experiments in cell biological studies. However, the cell confluence of the same culture may be diversely predicted by individual researchers. Herein, we designed a systematic quantification scheme implemented on the Matlab platform, the so-called "Confluence-Viewer" program, to assist cell biologists to better determine the cell confluence. Methods: Human normal oral fibroblasts (hOFs) seeded in 10 cm culture dishes were visualized under an inverted microscope for the acquisition of cell images. The images were subjected to the cell segmentation algorithm with top-hat transformation and the Otsu thresholding technique. A regression model was built using a quadratic model and shape-preserving piecewise cubic model. Results: The cell segmentation algorithm generated a regression curve that was highly correlated with the cell confluence determined by experienced researchers. However, the correlation was low when compared to the cell confluence determined by novice students. Interestingly, the cell confluence determined by experienced researchers became more diverse when they checked the same images without a time limitation (up to 1 min). Conclusion: This tool could prevent unnecessary human-made mistakes and meaningless repeats for novice researchers working on cell-based studies in health care or cancer research. [ABSTRACT FROM AUTHOR]

The interface friction characteristics of double-walled carbon nanotubes (DWCNTs) are studied using molecular dynamics simulations based on the Tersoff potential. The effects of the DWCNT type, outer shell diameter, and temperature are evaluated. The simulation results show that when an inner shell is being pulled out from a DWCNT, the friction force and normal force between shells increase with increasing the outer shell diameter. The noise of the friction force significantly increases with the increasing temperature. Zigzag@zigzag and armchair@armchair DWCNTs exhibit larger friction forces and smaller normal forces compared to those of chiral@chiral DWCNTs. [ABSTRACT FROM AUTHOR]

We propose a fully automated algorithm that is able to select a discriminative feature set from a training database via sequential forward selection (SFS), sequential backward selection (SBS), and F-score methods. We applied this scheme to microcalcifications cluster (MCC) detection in digital mammograms for early breast cancer detection. The system was able to select features fully automatically, regardless of the input training mammograms used. We tested the proposed scheme using a database of 111 clinical mammograms containing 1,050 microcalcifications (MCs). The accuracy of the system was examined via a free response receiver operating characteristic (fROC) curve of the test dataset. The system performance for MC identifications was Az = 0.9897, the sensitivity was 92%, and 0.65 false positives (FPs) were generated per image for MCC detection. [ABSTRACT FROM AUTHOR]

The synergic features and enhancing strategies for various photopolymerization systems are reviewed by kinetic schemes and the associated measurements. The important topics include (i) photo crosslinking of corneas for the treatment of corneal diseases using UVA-light (365 nm) light and riboflavin as the photosensitizer; (ii) synergic effects by a dual-function enhancer in a three-initiator system; (iii) synergic effects by a three-initiator C/B/A system, with electron-transfer and oxygen-mediated energy-transfer pathways; (iv) copper-complex (G1) photoredox catalyst in G1/Iod/NVK systems for free radical (FRP) and cationic photopolymerization (CP); (v) radical-mediated thiol-ene (TE) photopolymerizations; (vi) superbase photogenerator based-catalyzed thiol−acrylate Michael (TM) addition reaction; and the combined system of TE and TM using dual wavelength; (vii) dual-wavelength (UV and blue) controlled photopolymerization confinement (PC); (viii) dual-wavelength (UV and red) selectively controlled 3D printing; and (ix) three-wavelength selectively controlled in 3D printing and additive manufacturing (AM). With minimum mathematics, we present (for the first time) the synergic features and enhancing strategies for various systems of multi-components, initiators, monomers, and under one-, two-, and three-wavelength light. Therefore, this review provides not only the bridging between modeling and measurements, but also guidance for further experimental studies and new applications in 3D printings and additive manufacturing (AM), based on the innovative concepts (kinetics/schemes). [ABSTRACT FROM AUTHOR]

The aim of this study was to establish an early diagnostic system for the identification of the bone metastasis of prostate cancer in whole-body bone scan images by using a deep convolutional neural network (D-CNN). The developed system exhibited satisfactory performance for a small dataset containing 205 cases, 100 of which were of bone metastasis. The sensitivity and precision for bone metastasis detection and classification in the chest were 0.82 ± 0.08 and 0.70 ± 0.11, respectively. The sensitivity and specificity for bone metastasis classification in the pelvis were 0.87 ± 0.12 and 0.81 ± 0.11, respectively. We propose the use of hard example mining for increasing the sensitivity and precision of the chest D-CNN. The developed system has the potential to provide a prediagnostic report for physicians' final decisions. [ABSTRACT FROM AUTHOR]

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians' final decisions. [ABSTRACT FROM AUTHOR]

Lattice relaxation on wurtzite GaN microdisks grown by plasma-assisted molecular beam epitaxy was systematically studied. The lattice constants of GaN microdisks were evaluated from high-resolution transmission electron microscopy, and the anisotropic strain was then analyzed by observing the microscopic atomic layers. We found that the vertical lattice strain along the c-axis followed a linear relationship, while the lateral lattice strain along the a-axis exhibited a quadratic deviation. The lattice mismatch is about 0.94% at the interface between the GaN microdisks and the γ-LiAlO2 substrate, which induces the anisotropic strain during epi-growth. [ABSTRACT FROM AUTHOR]

In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring. [ABSTRACT FROM AUTHOR]

The kinetics and modeling of dual-wavelength (UV and blue) controlled photopolymerization confinement (PC) are presented and measured data are analyzed by analytic formulas and numerical data. The UV-light initiated inhibition effect is strongly monomer-dependent due to different C=C bond rate constants and conversion efficacies. Without the UV-light, for a given blue-light intensity, higher initiator concentration (C10) and rate constant (k') lead to higher conversion, as also predicted by analytic formulas, in which the total conversion rate (RT) is an increasing function of C1 and k'R, which is proportional to k'[gB1C1]0.5. However, the coupling factor B1 plays a different role that higher B1 leads to higher conversion only in the transient regime; whereas higher B1 leads to lower steady-state conversion. For a fixed initiator concentration C10, higher inhibitor concentration (C20) leads to lower conversion due to a stronger inhibition effect. However, same conversion reduction was found for the same H-factor defined by H0 = [b1C10 − b2C20]. Conversion of blue-only are much higher than that of UV-only and UV-blue combined, in which high C20 results a strong reduction of blue-only-conversion, such that the UV-light serves as the turn-off (trigger) mechanism for the purpose of spatial confirmation within the overlap area of UV and blue light. For example, UV-light controlled methacrylate conversion of a glycidyl dimethacrylate resin is formulated with a tertiary amine co-initiator, and butyl nitrite. The system is subject to a continuous exposure of a blue light, but an on-off exposure of a UV-light. Finally, we developed a theoretical new finding for the criterion of a good material/candidate governed by a double ratio of light-intensity and concentration, [I20C20]/[I10C10]. [ABSTRACT FROM AUTHOR]

Kinetics and analytical formulas for radical-mediated thiol–ene photopolymerization were developed in this paper. The conversion efficacy of thiol–ene systems was studied for various propagation to chain transfer kinetic rate-ratio (RK), and thiol–ene concentration molar-ratio (RC). Numerical data were analyzed using analytical formulas and compared with the experimental data. We demonstrated that our model for a thiol–acrylate system with homopolymerization effects, and for a thiol–norbornene system with viscosity effects, fit much better with the measured data than a previous model excluding these effects. The general features for the roles of RK and RC on the conversion efficacy of thiol (CT) and ene (CV) are: (i) for RK = 1, CV and CT have the same temporal profiles, but have a reversed dependence on RC; (ii) for RK >> 1, CT are almost independent of RC; (iii) for RK << 1, CV and CT have the same profiles and both are decreasing functions of the homopolymerization effects defined by kCV; (iv) viscosity does not affect the efficacy in the case of RK >> 1, but reduces the efficacy of CV for other values of RK. For a fixed light dose, higher light intensity has a higher transient efficacy but a lower steady-state conversion, resulting from a bimolecular termination. In contrast, in type II unimolecular termination, the conversion is mainly governed by the light dose rather than its intensity. For optically thick polymers, the light intensity increases with time due to photoinitiator depletion, and thus the assumption of constant photoinitiator concentration (as in most previous models) suffers an error of 5% to 20% (underestimated) of the crosslink depth and the efficacy. Scaling law for the overall reaction order, defined by [A]m[B]n and governed by the types of ene and the rate ratio is discussed herein. The dual ratio (RK and RC) for various binary functional groups (thiol–vinyl, thiol–acrylate, and thiol–norbornene) may be tailored to minimize side effects for maximal monomer conversion or tunable degree of crosslinking. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to explore a newly developed optical design, then to further improve the overhead lighting contrast in the laser projector module. In terms of the structural design of the projector, a liquid crystal lens array was used as the local dimming system for the light source, in order to achieve the objective, which was to significantly improve the contrast facility of the projection system. Second, in terms of the design of the light source, the output method for the light source was a laser light source employing arrays of micro-scanning. The main purpose was to compensate for the dim spots in the hole between the lenses in each unit of the liquid crystal when the liquid crystal lens array was locally dimmed, and thus significantly improving the contrast facility of the projection system. In terms of the software simulation, a liquid crystal lens array was used to simulate a pore size of 2.0 mm and focal lengths of 9 cm and 23 cm. The end effect gave good control and adjustment of the bright and dark areas during local dimming of the projector's imaging chip components. For a single laser source, the maximum contrast for local dimming was about 128:1, 438:1, and 244:1, for the Red (R), Green (G), and Blue (B) optical paths, respectively. The light efficiency scores were approximately 20.91%, 20.05%, and 24.45%, for the R, G, and B optical paths, respectively. After compensation using a micro-scanning light source, the defect of having dim spots between the pores was remedied, and the light adjustment area became more uniform while the contrasts became smaller. The maximum contrasts were approximately 52:1, 122:1, and 110:1, for the R, G, and B optical paths, respectively. For the projector, when the liquid crystal lenses were not transmissive, the maximum uniformity scores were 82.25%, 87.15%, and 88.43%, for the R, G, and B optical paths, respectively. [ABSTRACT FROM AUTHOR]

Optimal conditions for maximum efficacy of photoinitiated polymerization are theoretically presented. Analytic formulas are shown for the crosslink time, crosslink depth, and efficacy function. The roles of photoinitiator (PI) concentration, diffusion depth, and light intensity on the polymerization spatial and temporal profiles are presented for both uniform and non-uniform cases. For the type I mechanism, higher intensity may accelerate the polymer action process, but it suffers a lower steady-state efficacy. This may be overcome by a controlled re-supply of PI concentration during the light exposure. In challenging the conventional Beer–Lambert law (BLL), a generalized, time-dependent BLL (a Lin-law) is derived. This study, for the first time, presents analytic formulas for curing depth and crosslink time without the assumption of thin-film or spatial average. Various optimal conditions are developed for maximum efficacy based on a numerically-fit A-factor. Experimental data are analyzed for the role of PI concentration and light intensity on the gelation (crosslink) time and efficacy. [ABSTRACT FROM AUTHOR]

Cardiac stroke volume (SV) is an essential hemodynamic indicator that can be used to assess whether the pump function of the heart is normal. Non-invasive SV measurement is currently performed using the impedance cardiography (ICG). In this technology, left ventricular ejection time (LVET) is an important parameter which can be determined from the ICG signals. However, the ICG signals are inherently susceptible to artificial noise interference, which leads to an inaccurate LVET measurement and then yields an error in the calculation of SV. Therefore, the goal of the study was to measure LVETs using both the transmission and reflection photoplethysmography (PPG), and to assess whether the measured LVET was more accurate by the PPG signal than the ICG signal. The LVET measured by the phonocardiography (PCG) was used as the standard for comparing with those by the ICG and PPG. The study recruited ten subjects whose LVETs were simultaneously measured by the ICG using four electrodes, the reflection PPG using neck sensors (PPG neck ) and the transmission PPG using finger sensors (PPG finger ). In each subject, ten LVETs were obtained from ten heartbeats selected properly from one-minute recording. The differences of the measured LVETs between the PCG and one of the ICG, PPG neck and PPG finger were -68.2 ± 148.6 ms, 4.8 ± 86.5 ms and -7.0 ± 107.5 ms, respectively. As compared with the PCG, both the ICG and PPG finger underestimated but the PPG neck overestimated the LVETs. Furthermore, the measured LVET by the PPG neck was the closest to that by the PCG. Therefore, the PPG neck may be employed to improve the LVET measurement in applying the ICG for continuous monitoring of SV in clinical settings.

In the last decade, cuffless blood pressure measurement technology has been widely studied because it could be applied to a wearable apparatus. Electrocardiography (ECG), photo-plethysmography (PPG), and phonocardiography are always used to detect the pulse transit time (PTT) because the changed tendencies of the PTT and blood pressure have a negative relationship. In this study, the PPG signal was replaced by the impedance plethysmography (IPG) signal and was used to detect the PTT. The placement and direction of the electrode array for the IPG measurement were discussed. Then, we designed an IPG ring that could measure an accurate IPG signal. Twenty healthy subjects participated in this study. The changes in blood pressure after exercise were evaluated through the changes of the PTT. The results showed that the change of the systolic pressure had a better relationship with the change of the PTT IPG than that of the PTT PPG ( r = 0.700 vs. r = 0.450). Moreover, the IPG ring with spot electrodes would be more suitable to develop with the wearable cuffless blood pressure monitor than the PPG sensor., Competing Interests: The authors declare no conflict of interest. The founding sponsor had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.

This study proposes a fast 3D dynamic programming expansion to find a shortest surface in a 3D matrix. This algorithm can detect boundaries in an image sequence. Using phantom image studies with added uniform distributed noise from different SNRs, the unsigned error of this proposed method is investigated. Comparing the automated results to the gold standard, the best averaged relative unsigned error of the proposed method is 0.77% (SNR = 20 dB), and its corresponding parameter values are reported. We further apply this method to detect the boundary of the real superficial femoral artery (SFA) in MRI sequences without a contrast injection. The manual tracings on the SFA boundaries are performed by well-trained experts to be the gold standard. The comparisons between the manual tracings and automated results are made on 16 MRI sequences (800 total images). The average unsigned error rate is 2.4% (SD = 2.0%). The results demonstrate that the proposed method can perform qualitatively better than the 2D dynamic programming for vessel boundary detection on MRI sequences.

In this paper we propose a novel scheme able to automatically detect the intima and adventitia of both near and far walls of the common carotid artery in dynamic B-mode RF (radiofrequency) image sequences, with and without plaques. Via this automated system the lumen diameter changes along the heart cycle can be detected. Three image sequences have been tested and all results are compared to manual tracings made by two professional experts. The average errors for near and far wall detection are 0.058 mm and 0.067 mm, respectively. This system is able to analyze arterial plaques dynamically which is impossible to do manually due to the tremendous human workload involved.

From the underground parts of Picrorhiza scrophulariiflora, three new caffeoyl glycosides, scrocaffeside A-C (1-3), together with two caffeic acid derivates, 4-O-beta-D-glucopyranosyl caffeic acid (4) and 4-methoxy caffeic acid (5) and a phenylethanoid glycoside, scroside D (6), were isolated. Their structures were elucidated on the basis of chemical and spectroscopic evidence and comparisons with literature data of related compounds.

Many biomedical products have already been obtained from marine organisms. In order to search more therapeutic drugs against cancer, this study demonstrates the cytotoxicity effects of Cladiella australis, Clavularia viridis and Klyxum simplex extracts on human oral squamous cell carcinoma (SCC4, SCC9 and SCC25) cells using cell adhesion and cell viability assay. The morphological alterations in SCCs cells after treatment with three extracts, such as typical nuclear condensation, nuclear fragmentation and apoptotic bodies of cells were demonstrated by Hoechst stain. Flow cytometry indicated that three extracts sensitized SCC25 cells in the G(0)/G(1) and S-G(2)/M phases with a concomitant significantly increased sub-G(1) fraction, indicating cell death by apoptosis. This apoptosis process was accompanied by activation of caspase-3 expression after SCC25 cells were treated with three extracts. Thereby, it is possible that extracts of C. australis, C. viridis and K. simplex cause apoptosis of SCCs and warrant further research investigating the possible anti-oral cancer compounds in these soft corals.